基于LOG算子边缘检测提取特征点的平显振动算法研究

针对平显测试过程中图像抖动检测实时性要求较高的特点,通过对图像振动检测算法进行分析,提出了基于LOG算子边缘检测提取特征点的平显振动算法,利用该算法提取图像序列的特征点后并进行特征点匹配,根据特征点对位移量计算图像序列的振动量。实验结果表明该算法可以满足平显图像振动实时检测要求。     

关联挖掘下的海量文本信息深入挖掘实现

研究基于关联度挖掘的海量网络文本挖掘方法;随着计算机和网络技术的快速发展,网络上的文本呈现海量增长的趋势,传统的网络文本挖掘方法采用基于特征提取的方法实现,能够实现小数据量下的文本挖掘,但是在信息量的快速增长下,传统方法已经不能适应;提出一种基于关联度挖掘的海量网络文本挖掘方法,首先采用特征提取的方法对海量文本进行初步的分类和特征识别,然后采用关联度挖掘的方法对各个文本特征之间的关联度进行计算处理,根据关联度的大小最终实现文本挖掘,由于关联度可以很好的体现特征文本之间的相互关系;最后采用一组随机的网络热门词汇进行测试实验,结果显示,算法能够很好适应海量文本下的挖掘实现,具有很好的应用价值。     

3D information extraction using Region-based Deformable Net for monocular robot navigation

This paper extends the Region-based Deformable Net (RbDN) technique described in [1] to extract the 3D information of all the objects in the scene from a single moving camera. The technique is used for segmenting real-time video sequences captured from a single moving camera. The deformation process tracks the changes in the location and the shape of the segments across the frames. These changes along with the camera displacement are used to estimate the 3D information. The algorithm is completely autonomous and does not require pre-knowledge, training, or assumption about the contents of the sequence. It can handle the difficult case where the motion of the camera is parallel to its optical axis. It can also estimate the distances to objects that are more than 100 m away as long as the camera displacement is over 10% of the expected distance to the objects.     

基于伪zernike矩的车牌字符识别

基于伪zernike矩的不变性,提出了基于伪zernike矩特征提取的车牌字符识别方法。在车牌区域定位的基础上,通过对图像的二值化过程和字符图像分割等一系列处理,在进行归一化处理后分别提取伪zernike的高阶矩。将提取的伪zernike矩作为字符的特征描述输入到BP神经网络进行训练,最后进行车牌字符分类识别。通过实验证明了该方法的的可行性。测试结果表明,这种方法实用有效,识别效果优于HU矩和zernike矩。而且可以计算出错误率和可识别的最佳矩,减小了计算量且增强了字符识别的实时性。     

谐波提取理论的实践

介绍体效应器件的谐波工作机理和“基波振荡，谐波提取”的理论，根据该理论完成了6mm信号的放大和倍频，成功的设计了从X波段到3mm波段的八倍频组件，大大节约了雷达系统的成本和体积，实现了系统要求的性能。     

溶剂法浸出棉籽油和脱除棉酚的工艺研究

综述了采用单一溶剂或混合溶剂浸出棉仁生坯（或饼）,提取棉籽油并脱除棉籽粕中棉酚的工艺研究进展情况。     

双目视觉中的角点检测算法研究

为了实现双目视觉中的特征提取与立体匹配,在研究现有角点检测算法的基础上,提出一种改进的Harris角点检测算法。该算法首先采用图像分块和邻近角点剔除的策略,实现了阈值的自适应调节,并保证角点分布的均匀和避免角点聚簇的产生。结合Forstner检测算法,将精度提高到亚像素级。编程计算结果验证了该方法的准确性和稳定性。     

Kernel propagation strategy: A novel out-of-sample propagation projection for subspace learning

Kernel matrix optimization (KMO) aims at learning appropriate kernel matrices by solving a certain optimization problem rather than using empirical kernel functions. Since KMO is difficult to compute out-of-sample projections for kernel subspace learning, we propose a kernel propagation strategy (KPS) based on data distribution similar principle to effectively extract out-of-sample low-dimensional features for subspace learning with KMO. With KPS, we further present an example algorithm, i.e., kernel propagation canonical correlation analysis (KPCCA), which naturally fuses semi-supervised kernel matrix learning and canonical correlation analysis by means of kernel propagation projections. In KPCCA, the extracted correlation features of out-of-sample data not only incorporate integral data distribution information but also supervised information. Extensive experimental results have demonstrated the superior performance of our proposed method.     

图像语义提取方法研究

为解决从图像的低层视觉特征到高层语义特征的＂语义鸿沟＂问题,对当前的语义提取方法进行研究,简单介绍了图像语义层次模型,并根据语义信息的来源不同,归纳总结了图像语义中基于处理范围的方法,基于机器学习的方法,基于人机交互的方法和基于外部信息源的提取方法,这些工作为图像语义提取和图像语义检索等研究提供有益参考。